Abstract
A description is given of an asymptotically-minimum-variance algorithm for estimating the MA (moving-average) and ARMA (autoregressive moving-average) parameters of non-Gaussian processes from sample high-order moments. The algorithm uses the statistical properties (covariances and cross covariances) of the sample moments explicitly. A simpler alternative algorithm that requires only linear operations is also presented. The latter algorithm is asymptotically-minimum-variance in the class of weighted least-squares algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
MathematicsAutoregressive modelAsymptotically optimal algorithmAutoregressive–moving-average modelGaussianApplied mathematicsVariance (accounting)Estimation theoryAlgorithmStatistics
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Publication Info
- Year
- 1990
- Type
- article
- Volume
- 35
- Issue
- 1
- Pages
- 27-35
- Citations
- 131
- Access
- Closed
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Cite This
B. Friedlander,
B. Porat
(1990).
Asymptotically optimal estimation of MA and ARMA parameters of non-Gaussian processes from high-order moments.
IEEE Transactions on Automatic Control
, 35
(1)
, 27-35.
https://doi.org/10.1109/9.45140
Identifiers
- DOI
- 10.1109/9.45140